Hidden Markov model-based gesture recognition with FMCW radar

In this paper we present experimental results for the development of a gesture recognition system using a 77 GHz FMCW radar system. We measure the micro-Doppler signature of a gesturing hand to construct an energy distribution in velocity space over time. A gesturing hand is fundamentally a dynamical system with unobservable “state” (i.e. the name of the gesture) which determines the sequence of associated observable velocity-energy distributions, so a Hidden Markov Model is used to for gesture recognition, a more tailored approach than the SVM classifiers used in previous work. We also describe a method for reducing the length of our feature vectors by a factor of 12 without hurting the recognition performance, by reparameterizing them in terms of a sum of Gaussians.